自动识别计算机科学论文中的研究价值，覆盖32个子领域。
Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields
- 设计十类研究价值标注体系，实现大规模文本自动化分析
- 分析22.6万篇论文摘要，覆盖86个顶会/期刊，时间跨度十年
- 为理解不同领域研究动机提供量化工具，适合科研管理者与学者
计算机科学在过去几十年迅速发展，为多个领域提供计算工具与方法，并形成新的跨学科社区。这一发展深刻影响了机构实践和研究生态。因此，探索计算机科学相关研究群体所倡导的具体研究价值——即指导或激励研究态度与行为的基本信念——显得尤为重要。以往研究仅对少量机器学习论文进行人工分析。尚无研究在跨子领域的大规模科学文本中实现研究价值的自动检测。本文提出一个包含十类研究价值的详细标注方案，基于该方案构建价值分类器，对来自32个计算机科学子领域、86个主流出版物的226,600篇论文摘要开展系统性分析，时间跨度达十年。
原文摘要 · Abstract (English)
The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.
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